Static Plots: topic introduction

In this part of the course, we will cover the following concepts:

• Discuss the process of constructing complex plots
• Visualize data with ggplot2

Chat question

• Think about the data you analyze regularly at work
• Are you most likely to use univariate, bivariate, or multivariate plots to help interpret that data? Why?
• Share your responses in the chat

Module completion checklist

Visualizing with ggplot2

• ggplot2 is a tidyverse package used for creating graphics grammatically (i.e., “grammar of graphics,” or gg, plots)
• Building graphics layer by layer allows you to:
  • Explore your data efficiently
  • Communicate a visual story in a flexible and efficient way
  • Layer raw, summarized and contextual data
  • Reproduce and extend your work

Visualizing with ggplot2 (cont’d)

Working with ggplot2 package

• The ggplot2 package offers the following features:

Module completion checklist

Directory settings

• Let’s practice using ggplot2 to visualize a sample dataset
• In order to maximize the efficiency of your workflow, we use the box package and encode your directory structure into variables
• Let the main_dir be the variable corresponding to your materials folder

# Set `main_dir` to the location of your materials folder.

path = box::file()
main_dir = dirname(dirname(path))

Directory settings (cont’d)

# Make `data_dir` from the `main_dir` and
# remainder of the path to data directory.
data_dir = paste0(main_dir, "/data")
# Make `plots_dir` from the `main_dir` and
# remainder of the path to plots directory.
plot_dir = paste0(main_dir, "/plots")

Case study: stroke survey

Stroke Dataset: attribute information

• id: unique identifier
• gender: “Male”, “Female” or “Other”
• age: age of the patient
• hypertension: 0 if the patient doesn’t have hypertension, 1 if the patient has hypertension
• heart_disease: 0 if the patient doesn’t have any heart diseases, 1 if the patient has a heart disease
• ever_married: “No” or “Yes”
• work_type: “children”, “Govt_job”, “Never_worked”, “Private” or “Self-employed”
• Residence_type: “Rural” or “Urban”
• avg_glucose_level: average glucose level in blood
• bmi: body mass index
• smoking_status: “formerly smoked”, “never smoked”, “smokes” or “Unknown”*
• stroke: 1 if the patient had a stroke or 0 if not

Load the dataset

• Let’s load the dataset from our data_dir into R’s environment

# Read CSV file called "healthcare-dataset-stroke-data.csv"
health_data = read.csv(file = file.path(data_dir,"healthcare-dataset-stroke-data.csv"), #<- provide file path
               header = TRUE,            #<- if file has header set to TRUE
               stringsAsFactors = FALSE) #<- read strings as characters, not as factors

View data types

• Let’s examine the data types of the columns in the dataset and handle the missing data, if there are any

str(health_data)

'data.frame':   5110 obs. of  12 variables:
 $ id               : int  9046 51676 31112 60182 1665 56669 53882 10434 27419 60491 ...
 $ gender           : chr  "Male" "Female" "Male" "Female" ...
 $ age              : num  67 61 80 49 79 81 74 69 59 78 ...
 $ hypertension     : int  0 0 0 0 1 0 1 0 0 0 ...
 $ heart_disease    : int  1 0 1 0 0 0 1 0 0 0 ...
 $ ever_married     : chr  "Yes" "Yes" "Yes" "Yes" ...
 $ work_type        : chr  "Private" "Self-employed" "Private" "Private" ...
 $ Residence_type   : chr  "Urban" "Rural" "Rural" "Urban" ...
 $ avg_glucose_level: num  229 202 106 171 174 ...
 $ bmi              : num  36.6 NA 32.5 34.4 24 29 27.4 22.8 NA 24.2 ...
 $ smoking_status   : chr  "formerly smoked" "never smoked" "never smoked" "smokes" ...
 $ stroke           : int  1 1 1 1 1 1 1 1 1 1 ...

Impute missing data

• We will now impute missing values in the bmi column with the mean

# Convert BMI to numeric
health_data$bmi <- as.numeric(health_data$bmi)
# Replace N/A's in BMI column with mean
health_data$bmi[is.na(health_data$bmi)] <- mean(health_data$bmi,na.rm=TRUE)

str(health_data)

'data.frame':   5110 obs. of  12 variables:
 $ id               : int  9046 51676 31112 60182 1665 56669 53882 10434 27419 60491 ...
 $ gender           : chr  "Male" "Female" "Male" "Female" ...
 $ age              : num  67 61 80 49 79 81 74 69 59 78 ...
 $ hypertension     : int  0 0 0 0 1 0 1 0 0 0 ...
 $ heart_disease    : int  1 0 1 0 0 0 1 0 0 0 ...
 $ ever_married     : chr  "Yes" "Yes" "Yes" "Yes" ...
 $ work_type        : chr  "Private" "Self-employed" "Private" "Private" ...
 $ Residence_type   : chr  "Urban" "Rural" "Rural" "Urban" ...
 $ avg_glucose_level: num  229 202 106 171 174 ...
 $ bmi              : num  36.6 28.9 32.5 34.4 24 ...
 $ smoking_status   : chr  "formerly smoked" "never smoked" "never smoked" "smokes" ...
 $ stroke           : int  1 1 1 1 1 1 1 1 1 1 ...

Subsetting data

• Suppose we only want to work on a few of these variables, not the entire dataset; we must restructure our data by taking a subset of the data with all observations of the following variables:

  • age variable
  • avg_glucose_level variable
  • bmi variable

• Since we know which columns we want to subset in the dataset, we can use their column names to extract them

health_subset <- health_data[, c("age", "avg_glucose_level", "bmi")]
str(health_subset)

'data.frame':   5110 obs. of  3 variables:
 $ age              : num  67 61 80 49 79 81 74 69 59 78 ...
 $ avg_glucose_level: num  229 202 106 171 174 ...
 $ bmi              : num  36.6 28.9 32.5 34.4 24 ...

Installing ggplot2

• Since ggplot2 is an external package, we need to install it first

# First let's install `ggplot2`. if not installed
#install.packages("ggplot2")

# Then we need to load it to our environment.
library(ggplot2)

# Take a look at the documentation.
library(help = "ggplot2")

Working with ggplot2 package

?ggplot

Working with ggplot2 package (cont’d)

Plotting with ggplot2: setup

• Let’s start by creating a histogram to show the distribution of the age

• In order to make a base plot we need to:

  • Specify data
  • Link data to visuals
  • Assign shapes

Plotting with ggplot2: setup

• Translated into ggplot2 syntax, this will require us to call a ggplot function and:

  • Set its first argument to the data we want to use (e.g. health_subset)
  • Set its second argument to the mapping of our choice (e.g. Age to be plotted on x-axis)
  • Add a geom layer, in our case it will be a histogram (e.g. add geom_histogram to the base plot)

Making a plot with geom_histogram

# Let's create a base plot.
ggp1 = ggplot(health_subset,    #<-Set data
              aes(x = age))#<-Set aesthetics  
ggp1

# Let's add a layer.
# Each layer is called a `geom`, we need to use
# `geom_histogram` to add a histogram layer.
ggp1 + geom_histogram()

Building a histogram using density with ggplot2

ggp1 + geom_histogram()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Plotting with ggplot2: adjust

• To make the visualization prominent,we need to adjust:

  • Visual effects
  • Axes
  • Legend

Plotting with ggplot2: adjust (cont’d)

• Depending on the plot and the final goal, you may want to:

  • Change colors, add more layers with extra information such as additional data sources, reference lines or points, etc.
  • Add axes labels and adjust the breaks in data (i.e. change scale, bins in the histogram)
  • Add a legend if there are more variables or datasets than one combined on the plot

Making a plot with geom_histogram: adjust

# Say instead of frequency counts we want
# probability density estimates. We can:
ggp1 +
  # 1. Make `y-axis` of type `density`
  geom_histogram(aes(y = ..density..),
  # 2. Adjust binwidth for better smoothness.
                 binwidth = 10)

ggp1 = ggp1 +    #<- save adjusted plot
  geom_histogram(aes(y = ..density..),
                 binwidth = 10,
                 # 3. Add color & fill.
                 color = "steelblue",
                 fill = "gray")
ggp1             #<- view saved plot

Plotting with ggplot2: polish

Plotting with geom_histogram: polish

ggp1 = ggp1 + 
  # Add density layer with 50% opaque fill 
  # in "steelblue" and gray color (border).
  geom_density(alpha = .5, 
               color = "gray",
               fill = "steelblue")
ggp1

ggp1  = ggp1 + 
# Add plot title and subtitle.
labs(title = "Age Distribution",
     subtitle = "Histogram & Density")
ggp1

Plotting with geom_histogram: polish (cont’d)

# Add a black and white theme to
# overwrite default.
ggp1 = ggp1 + 
  
  # Add a black and white theme.
  theme_bw() +
  
  # Customize elements of the theme.
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        plot.title = element_text(size = 25),
        plot.subtitle = element_text(size = 18))

# Display polished plot.
ggp1

Scatterplot with geom_point: set up

• The histogram was a univariate visualization, so we only had to define a single axis

• But for bivariate visualizations, like scatterplots, we need to define 2 axes:

  • x-axis
  • y-axis

• In ggplot terms, this means that we need to map 2 aes parameters (x and y respectively)

• Let’s plot the relationship between age on x-axis and avg_glucose_level on y-axis

Scatterplot with geom_point: set up

# Let's create a base plot.
ggp2 = ggplot(health_subset,                   
              aes(x = age,
                  y = avg_glucose_level)) 
ggp2

# Now, let's add a layer.
# Each layer is called a `geom`, we need to use
# `geom_point` to add a histogram layer.
ggp2 = ggp2 + geom_point()
ggp2

Building a scatterplot with fitted line

ggp2

Adjusting a scatterplot with geom_point

ggp2 = ggp2 + 
  
  # Adjust the color of the points.
  geom_point(color = "darkorange") + 
  
  # Add linear regression line (`lm`). 
  geom_smooth(method = lm) +
  
  # Add a title and a subtitle.
  labs(title = "age vs. avg_glucose_level",
       subtitle = "Scatterplot with linear fit")

# View the plot.
ggp2

`geom_smooth()` using formula 'y ~ x'

Polishing a scatterplot with geom_point

ggp2 +
  
  # Add black & white theme, adjust labels.
  theme_bw() + 
  
  # Customize elements of the theme.
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        plot.title = element_text(size = 25),
        plot.subtitle = element_text(size = 18))

`geom_smooth()` using formula 'y ~ x'

Saving a theme to a variable

• We’ve used the following 3 lines of code for both the charts:

theme_bw() + 
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        plot.title = element_text(size = 25),
        plot.subtitle = element_text(size = 18))

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  ..$ inherit.blank: logi FALSE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 $ plot.caption              :List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : NULL
  ..$ size         : 'rel' num 0.8
  ..$ hjust        : num 1
  ..$ vjust        : num 1
  ..$ angle        : NULL
  ..$ lineheight   : NULL
  ..$ margin       : 'margin' num [1:4] 5.5pt 0pt 0pt 0pt
  .. ..- attr(*, "valid.unit")= int 8
  .. ..- attr(*, "unit")= chr "pt"
  ..$ debug        : NULL
  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 $ plot.caption.position     : chr "panel"
 $ plot.tag                  :List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : NULL
  ..$ size         : 'rel' num 1.2
  ..$ hjust        : num 0.5
  ..$ vjust        : num 0.5
  ..$ angle        : NULL
  ..$ lineheight   : NULL
  ..$ margin       : NULL
  ..$ debug        : NULL
  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 $ plot.tag.position         : chr "topleft"
 $ plot.margin               : 'margin' num [1:4] 5.5pt 5.5pt 5.5pt 5.5pt
  ..- attr(*, "valid.unit")= int 8
  ..- attr(*, "unit")= chr "pt"
 $ strip.background          :List of 5
  ..$ fill         : chr "grey85"
  ..$ colour       : chr "grey20"
  ..$ size         : NULL
  ..$ linetype     : NULL
  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_rect" "element"
 $ strip.background.x        : NULL
 $ strip.background.y        : NULL
 $ strip.placement           : chr "inside"
 $ strip.text                :List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : chr "grey10"
  ..$ size         : 'rel' num 0.8
  ..$ hjust        : NULL
  ..$ vjust        : NULL
  ..$ angle        : NULL
  ..$ lineheight   : NULL
  ..$ margin       : 'margin' num [1:4] 4.4pt 4.4pt 4.4pt 4.4pt
  .. ..- attr(*, "valid.unit")= int 8
  .. ..- attr(*, "unit")= chr "pt"
  ..$ debug        : NULL
  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 $ strip.text.x              : NULL
 $ strip.text.y              :List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : NULL
  ..$ size         : NULL
  ..$ hjust        : NULL
  ..$ vjust        : NULL
  ..$ angle        : num -90
  ..$ lineheight   : NULL
  ..$ margin       : NULL
  ..$ debug        : NULL
  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 $ strip.switch.pad.grid     : 'unit' num 2.75pt
  ..- attr(*, "valid.unit")= int 8
  ..- attr(*, "unit")= chr "pt"
 $ strip.switch.pad.wrap     : 'unit' num 2.75pt
  ..- attr(*, "valid.unit")= int 8
  ..- attr(*, "unit")= chr "pt"
 $ strip.text.y.left         :List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : NULL
  ..$ size         : NULL
  ..$ hjust        : NULL
  ..$ vjust        : NULL
  ..$ angle        : num 90
  ..$ lineheight   : NULL
  ..$ margin       : NULL
  ..$ debug        : NULL
  ..$ inherit.blank: logi TRUE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 - attr(*, "class")= chr [1:2] "theme" "gg"
 - attr(*, "complete")= logi TRUE
 - attr(*, "validate")= logi TRUE

Saving a theme to a variable (cont’d)

my_ggtheme = theme_bw() + 
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        plot.title = element_text(size = 25),
        plot.subtitle = element_text(size = 18))

# Add saved theme and re-save the plot.
ggp2 = ggp2 + my_ggtheme
ggp2

`geom_smooth()` using formula 'y ~ x'

Knowledge Check

Module completion checklist

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